Method and system for determining correctness of lidar sensor data used for localizing autonomous vehicle

ABSTRACT

Disclosed herein is method and system for determining correctness of Lidar sensor data used for localizing autonomous vehicle. The system identifies one or more Region of Interests (ROIs) in Field of View (FOV) of Lidar sensors of autonomous vehicle along a navigation path. Each ROI includes one or more objects. Further, for each ROI, system obtains Lidar sensor data comprising one or more reflection points corresponding to the one or more objects. The system forms one or more clusters in each ROI. The system identifies a distance value between, one or more clusters projected on 2D map of environment and corresponding navigation map obstacle points, for each ROI. The system compares distance value between one or more clusters and obstacle points based on which correctness of Lidar sensor data is determined. In this manner, present disclosure provides a mechanism to detect correctness of Lidar sensor data for navigation in real-time.

TECHNICAL FIELD

The present subject matter is generally related to autonomous vehiclesand more particularly, but not exclusively, to a method and a system fordetermining correctness of Lidar sensor data used for localizingautonomous vehicle.

BACKGROUND

Autonomous vehicles may be equipped with various types of sensors suchas, Lidar, sonar, radar, cameras, and other sensors to detect objects inits environment. The autonomous vehicles using Lidar as the main sensingdevice, requires correct Lidar data points all the time, for localizingthe vehicle itself on navigation map. This is a continuous process, butwhile moving, the autonomous vehicles may face many uncertainties on itsvision. The uncertainties may be due to a snowfall which blocks theLidar vision partially and cause an error in received Lidar data points.Similarly, heavy drops on Lidar screen may cause lens effect or fallingtree leaf just wrapped the lidar face for few seconds, may lead to anerror in the collected Lidar data points. Further, performance of theLidar sensor suffers when the weather condition becomes adverse andvisibility range decreases.

The existing mechanisms provide a comprehensive model for detectingobjects in vehicle's environment. The mechanisms adjust one or morecharacteristics of the model based on the received weather informationto account for an impact of the actual or expected weather conditions onone or more of the plurality of sensors. However, the existing mechanismdoes not disclose correction of the Lidar point data in real-time whichhas occurred due to natural things, or incurred any damage, or alignmentissue, which hinders Lidar vision.

The information disclosed in the background of the disclosure section isonly for enhancement of understanding of the general background of theinvention and should not be taken as an acknowledgement or any form ofsuggestion that this information forms the prior art already known to aperson skilled in the art.

SUMMARY

Disclosed herein is a method of determining correctness of Lidar sensordata used for localizing an autonomous vehicle. The method comprisesidentifying, by a detection system, one or more Region of Interests(ROIs) in Field of View (FOV) of Lidar sensors of the autonomous vehiclealong a navigation path. Thereafter, the method comprises obtainingLidar sensor data comprising one or more reflection points in each ofthe one or more ROIs. The one or more reflection points correspond toone or more objects in each of the one or more ROIs. Then the methodcomprises forming one or more clusters of one or more reflection pointsin each of the one or more ROIs. Once the one or more clusters areformed, the method comprises identifying a distance value between theone or more clusters projected on a 2D map of an environment andcorresponding navigation map obstacle points for each of the one or moreROIs. Finally, the method comprises determining correctness of the Lidarsensor data based on the distance value.

Further, the present disclosure discloses a system for determiningcorrectness of Lidar sensor data used for localizing an autonomousvehicle. The system comprises a processor and a memory communicativelycoupled to the processor. The memory stores processor-executableinstructions, which, on execution, causes the processor to identify oneor more Region of Interests (ROIs) in Field of View (FOV) of Lidarsensors of the autonomous vehicle along a navigation path. Thereafter,the processor obtains Lidar sensor data comprising one or morereflection points, in each of the one or more ROIs, wherein the one ormore reflection points correspond to one or more objects in each of theone or more ROIs. The processor forms one or more clusters of one ormore reflection points in each of the one or more ROIs upon receivingthe Lidar sensor data. Thereafter, the processor identifies a distancevalue between the one or more clusters projected on a 2D map of anenvironment and corresponding navigation map obstacle points, for eachof the one or more ROIs, to determine the correctness of the Lidarsensor data.

Furthermore, the present disclosure comprises a non-transitory computerreadable medium including instructions stored thereon that whenprocessed by at least one processor causes detection system fordetermining correctness of Lidar sensor data used for localizing anautonomous vehicle to identify one or more Region of Interests (ROIs) inField of View (FOV) of Lidar sensors of an autonomous vehicle along anavigation path. Further, the instructions cause the processor to obtainLidar sensor data comprising one or more reflection points, in each ofthe one or more ROIs, wherein the one or more reflection pointscorrespond to one or more objects in each of the one or more ROIs.Furthermore, the instructions cause the processor to form one or moreclusters of one or more reflection points in each of the one or moreROIs. In addition, the instructions cause the processor to identify adistance value between the one or more clusters projected on a 2D map ofan environment and corresponding navigation map obstacle points, foreach of the one or more ROIs. Finally, the instructions cause theprocessor to determine correctness of the Lidar sensor data based on thedistance value.

The foregoing summary is illustrative only and is not intended to be inany way limiting. In addition to the illustrative aspects, embodiments,and features described above, further aspects, embodiments, and featureswill become apparent by reference to the drawings and the followingdetailed description.

BRIEF DESCRIPTION OF THE ACCOMPANYING DRAWINGS

The accompanying drawings, which are incorporated in and constitute apart of this disclosure, illustrate exemplary embodiments and, togetherwith the description, explain the disclosed principles. In the figures,the left-most digit(s) of a reference number identifies the figure inwhich the reference number first appears. The same numbers are usedthroughout the figures to reference like features and components. Someembodiments of system and/or methods in accordance with embodiments ofthe present subject matter are now described, by way of example only,and regarding the accompanying figures, in which:

FIG. 1 shows an exemplary architecture for determining correctness ofLidar sensor data used for localizing an autonomous vehicle inaccordance with some embodiments of the present disclosure.

FIG. 2a shows a block diagram of a detection system in accordance withsome embodiments of the present disclosure.

FIGS. 2b-2e illustrates an exemplary method for determining correctnessof Lidar sensor data using exemplary Region of Interest (ROIs).

FIG. 3 shows a flowchart illustrating a method of determiningcorrectness of Lidar sensor data used for localizing an autonomousvehicle in accordance with some embodiments of the present disclosure.

FIG. 4 illustrates a block diagram of an exemplary computer system forimplementing embodiments consistent with the present disclosure.

It should be appreciated by those skilled in the art that any blockdiagram herein represent conceptual views of illustrative systemsembodying the principles of the present subject matter. Similarly, itwill be appreciated that any flow chart, flow diagram, state transitiondiagram, pseudo code, and the like represent various processes, whichmay be substantially represented in computer readable medium andexecuted by a computer or a processor, whether such computer orprocessor is explicitly shown.

DETAILED DESCRIPTION

In the present document, the word “exemplary” is used herein to mean“serving as an example, instance, or illustration”. Any embodiment orimplementation of the present subject matter described herein as“exemplary” is not necessarily to be construed as preferred oradvantageous over other embodiments.

While the disclosure is susceptible to various modifications andalternative forms, specific embodiment thereof has been shown by way ofexample in the drawings and will be described in detail below. It shouldbe understood, however that it is not intended to limit the disclosureto the specific forms disclosed, but on the contrary, the disclosure isto cover all modifications, equivalents, and alternatives falling withinthe scope of the disclosure.

The terms “comprises”, “comprising”, “includes”, “including” or anyother variations thereof, are intended to cover a non-exclusiveinclusion, such that a setup, device, or method that comprises a list ofcomponents or steps, does not include only those components or steps,but may include other components or steps, not expressly listed orinherent to such setup or device or method. In other words, one or moreelements in a system or apparatus proceeded by “comprises . . . a” doesnot, without more constraints, preclude the existence of other elementsor additional elements in the system or method.

The present disclosure relates to method and system for determiningcorrectness of Lidar sensor data used for localizing an autonomousvehicle. The system may identify one or more Region of Interests (ROIs)in Field of View (FOV) of Lidar sensors of the autonomous vehicle alonga navigation path. Each ROI may include one or more objects. As anexample, the one or more objects may include, but not limited to,buildings and vehicles. Further, for each ROI, the system may obtainLidar sensor data from the Lidar sensor. The Lidar sensor data comprisesone or more reflection points corresponding to the one or more objects.The system may form one or more clusters in each ROI wherein theclusters comprises one or more reflection points. The system may formthe one or more clusters by joining the one or more reflection pointsuntil a pattern is formed which is of a predefined length. The patternmay be such as a straight line, arc, L-shape or multiple bend shape. Inan embodiment, once the one or more clusters are formed, the system mayidentify a distance value between the one or more clusters projected ona 2D map of an environment and corresponding navigation map obstaclepoints, for each of the one or more ROIs. The navigation map obstaclepoints may be obtained from a navigation map associated with theautonomous vehicle when the vehicle is moving from a current position toa destination position. The system compares distance value between theone or more clusters and the obstacle points on the navigation map. Ifthe distance value is less than a predefined threshold value, the systemmay detect the Lidar sensor data as correct. However, if the distancevalue is more than the predefined threshold value, the system may detectthe Lidar sensor data as incorrect and provide information to anavigation module of the autonomous vehicle to discontinue navigation.In this manner, the present disclosure provides a mechanism to detectcorrectness of Lidar sensor data for navigation in real-time.

FIG. 1 shows an exemplary architecture 100 for determining correctnessof Lidar sensor data used for localizing an autonomous vehicle 101 inaccordance with some embodiments of the present disclosure.

The architecture 100 may include an autonomous vehicle 101 [alsoreferred as vehicle 101], a detection system 105 and a database 103. Thedatabase 103 may be configured to store a navigation map. In someembodiments, the detection system 105 may be configured within theautonomous vehicle 101 as shown in the FIG. 1. In some otherembodiments, the detection system 105 may be remotely associated withthe vehicle 101, via a wireless communication network (not shown). As anexample, the vehicle 101 may be a bike, a car, a truck, a bus and thelike. The vehicle 101 may be configured with Lidar sensors to aid innavigating the vehicle 101 from a source position to a destinationposition.

The detection system 105 may include a processor 107, an Input/Output(I/O) interface 109 and a memory 111. The I/O interface 109 may beconfigured to receive navigation map data from the database 103. Theprocessor 107 may be configured to identify one or more Region ofInterests (ROIs) in a Field of View of the Lidar sensors along anavigation path of the vehicle 101. Each ROI may include one or moreobjects such as building, vehicles and any other man-made objects. Uponidentifying the one or more ROIs, the processor 107 may obtain Lidarsensor data from the Lidar sensors. The Lidar sensor data may comprisereflection points of the objects in the ROI. In an embodiment, theprocessor 107 may form one or more clusters of one or more reflectionpoints lying on the same horizontal plane in each ROI. The one or moreclusters may be formed by joining the one or more reflection pointsuntil a pattern of a predefined length is formed. As an example, thepattern may include, but not limited to, straight line, arc, L-shape, ormultiple bend shape. In one embodiment, the predefined length may be onemeter. The one or more clusters may be projected on a 2D environment mapassociated with the autonomous vehicle 101.

In an embodiment, once the one or more clusters are formed, theprocessor 107 may identify distance value between the one or moreclusters projected on the 2D map and corresponding navigation mapobstacle points. The navigation map obstacle points may be obtained fromthe database 103. The navigation map obstacle points correspond to thesame ROI for which the processor 107 has formed the one or moreclusters. In an embodiment, the processor 107 may identify a distancevalue between the one or more clusters projected on the 2D map with thecorresponding navigation map obstacle points. In an embodiment, if thedistance value is less than a predefined threshold value, the processor107 may detect the Lidar sensor data as correct. Otherwise, theprocessor 107 may detect the Lidar sensor data as incorrect. In anotherembodiment, if the distance values of 75% of the total clusters are lessthan a predefined threshold value, the processor 107 may detect theLidar sensor data as correct. Otherwise, the processor 107 may detectthe Lidar sensor data as incorrect. Once the Lidar sensor data isdetected as incorrect, the processor 107 may provide informationregarding incorrect Lidar sensor data to a navigation module of theautonomous vehicle 101 to discontinue the navigation or to look for asafe parking on roadside of the vehicle 101.

FIG. 2a shows a block diagram of a detection system in accordance withsome embodiments of the present disclosure.

In some implementations, the system may include data 200 and modules210. As an example, the data 200 is stored in a memory 111 configured inthe system as shown in the FIG. 2a . In one embodiment, the data 200 mayinclude ROI data 201, Lidar sensor data 203, cluster data 205 and otherdata 209. In the illustrated FIG. 2a , modules are described here indetail.

In some embodiments, the data 200 may be stored in the memory 111 inform of various data structures. Additionally, the data 200 can beorganized using data models, such as relational or hierarchical datamodels. The other data 209 may store data, including temporary data andtemporary files, generated by the modules for performing the variousfunctions of the system.

In some embodiments, the data 200 stored in the memory 111 may beprocessed by the modules 210 of the system. The modules 210 may bestored within the memory 111. In an example, the modules 210communicatively coupled to the processor 107 configured in the system,may also be present outside the memory 111 as shown in FIG. 2a andimplemented as hardware. As used herein, the term modules 210 may referto an Application Specific Integrated Circuit (ASIC), an electroniccircuit, a processor (shared, dedicated, or group) and memory 111 thatexecute one or more software or firmware programs, a combinational logiccircuit, and/or other suitable components that provide the describedfunctionality.

In some embodiments, the modules 210 may include, for example, ROIidentification module 211, Lidar sensor data obtaining module 213, acluster forming module 215, a distance value identification module 217,a correctness determination module 219 and other modules 221. The othermodules 221 may be used to perform various miscellaneous functionalitiesof the system. It will be appreciated that such aforementioned modulesmay be represented as a single module or a combination of differentmodules.

In an embodiment, the other modules 221 may be used to perform variousmiscellaneous functionalities of the system. It will be appreciated thatsuch modules may be represented as a single module or a combination ofdifferent modules. Furthermore, a person of ordinary skill in the artwill appreciate that in an implementation, the one or more modules maybe stored in the memory 111, without limiting the scope of thedisclosure. The said modules when configured with the functionalitydefined in the present disclosure will result in a novel hardware.

In an embodiment, the ROI identification module 211 may be configured toidentify one or more ROIs in Field of View (FOV) of Lidar sensorsconfigured in the vehicle 101. As an example, the autonomous vehicle 101may be navigating from a source position to a destination position andhence a reference navigation path as shown in FIG. 2b from the sourceposition to the destination position may be obtained from the database103. The reference navigation path may comprise one or more path points.The ROI identification module 211 may consider the one or more pathpoints for a predefined distance range. As an example, the predefineddistance range may be 5-20 meters. So, 20 meters from the currentposition of the vehicle 101, the one or more path points may beconsidered for further processing. As shown in FIG. 2b , there may beone or more objects such as building, building 1, building 2, building 3and so on alongside the vehicle 101 when the vehicle 101 is navigatingfrom the source position to the destination position. However, not allthe objects may act as an obstacle for the vehicle 101 and hence it isnecessary to identify those objects which are not in field of view ofthe Lidar Sensor configured in the vehicle 101. Therefore, the ROIidentification module 211 may obtain the one or more path points in thepredefined distance range which is 20 meters from the source position ofthe vehicle 101. Once the one or more path points are obtained, the ROIidentification module 211 may form one or more straight lines ofpredefined number of path points. As an example, the predefined numberof path points may be five. Therefore, the one or more straight linesare formed along the navigation path from 5 meters distance to 20 metersdistance to identify one or more tangents. As shown in FIG. 2b there are3 tangents, tangent 1, tangent 2 and tangent 3. Once the tangents areidentified, the ROI module may identify the one or more ROIs in adirection perpendicular to each of the one or more tangents. As shown inFIG. 2b , the lines, line 1 and line 2 are perpendicular to tangent 1,line 3 is perpendicular to tangent 2 and lines 4 and 5 are perpendicularto the tangent 3. The one or more ROIs such as ROI 1, ROI 2, ROI 3 andROI 4 are identified based on the direction of the lines from thetangents as shown in FIG. 2b . The identified ROIs may be stored as ROIdata 201.

In an embodiment, the Lidar sensor data obtaining module 213 [alsoreferred as obtaining module 213 may be configured to obtain Lidarsensor data 203 associated with one or more objects in each of theidentified ROIs. Each ROI may include one or more objects such asbuildings and vehicles. The obtaining module 213 may obtain the Lidarsensor data 203 corresponding to the objects in each ROI. The Lidarsensor data 203 may comprise one or more reflection points 252 whichcorresponds to the objects in the ROI. As an example, two ROIs, ROI 1and ROI 2 are shown in FIG. 2c . FIG. 2c also shows reflection points252 of the objects in each ROI, ROI 1 and ROI 2.

In an embodiment, the cluster forming module 215 may be configured toform one or more clusters of one or more reflection points 252 in eachROI. The one or more clusters may be formed by joining the one or morereflection 252 points until a pattern is formed which is of a predefinedlength. As an example, the predefined length may be 1 meter and thepattern may be a straight line, arc, L-shape, or multiple bend shape.FIG. 2d shows one or more clusters formed in each ROI. As an example,the ROI 1 may include two clusters of two straight lines, straight line1 and straight line 2 and the ROI 2 may comprise three clusters of threestraight lines, straight line 1, straight line 2 and straight line 3.The formed clusters may be stored as cluster data 205.

In an embodiment, the distance value identification module 217 may beconfigured to identify the distance between the one or more clusters andthe corresponding obstacle points in navigation map. The obstacle pointscorresponding to the ROI 1 may be obtained from the navigation map whichis as shown in FIG. 2e . FIG. 2e shows the reflection points 252 of twostraight lines, straight line 1 and straight line 2. The distance valuemay be identified using existing distance calculation methods betweentwo points i.e. the reflection points 252 and the obstacle points. In anembodiment, if the distance value is less than the predefined thresholdvalue, the correctness determination module 219 may detect the Lidarsensor data 203 as correct. In another embodiment, if the distance valueis less than a predefined threshold value for 80 percent of theidentified clusters in each ROI, then the correctness determinationmodule 219 may detect the Lidar sensor data 203 as correct. As anexample, the predefined threshold value may be 1.5 meter. If thedistance value is greater than the predefined threshold value, then thecorrectness determination module 219 may detect the Lidar sensor data203 as correct. Once the Lidar sensor data 203 is detected as incorrect,the processor 107 may provide information regarding incorrect Lidarsensor data 203 to a navigation module of the autonomous vehicle 101 todiscontinue the navigation or to look for a safe parking on roadside ofthe vehicle 101.

FIG. 3 shows a flowchart illustrating method for determining correctnessof Lidar sensor data used for localizing an autonomous vehicle 101 inaccordance with some embodiments of the present disclosure.

As illustrated in FIG. 3, the figure includes one or more blocksillustrating a method for determining correctness of Lidar sensor data203 used for localizing an autonomous vehicle 101. The method 300 may bedescribed in the general context of computer executable instructions.Generally, the computer executable instructions can include routines,programs, objects, components, data structures, procedures, modules, andfunctions, which perform specific functions or implement specificabstract data types.

The order in which the method is described is not intended to beconstrued as a limitation, and any number of the described method blockscan be combined in any order to implement the method. Additionally,individual blocks may be deleted from the methods without departing fromthe spirit and scope of the subject matter described herein.Furthermore, the method can be implemented in any suitable hardware,software, firmware, or combination thereof.

At block 301, the method may include identifying one or more ROIs in FOVof Lidar sensors 250 of the vehicle 101. The one or more ROIs may beidentified along the navigation path of the vehicle 101 from a sourceposition to a destination position. The ROIs may include one or moreobjects such as buildings and vehicles.

At block 303, the method may include obtaining Lidar sensor data 203 ineach of the one or more ROIs. The Lidar sensor data 203 may comprise oneor more reflection points 252 which corresponds to the objects in theROI.

At block 305, the method includes forming one or more clusters of one ormore reflection points 252 in each of the one or more ROIs. The one ormore clusters may be formed by joining the one or more reflection points252 until a pattern is formed which is of a predefined length. As anexample, the predefined length may be 1 meter and the pattern may be astraight line, arc, L-shape, or multiple bend shape.

At block 307, the method may include identifying a distance valuebetween the one or more clusters and navigation map obstacle points foreach of the one or more ROIs. The navigation map obstacle pointscorresponding to each ROI may be obtained from the navigation map. Thedistance value may be identified using existing distance calculationmethods between two points. The distance value such as d1, d2, d3 and soon are determined which are distance between the obstacle points andcluster points in each cluster which may be less than 5 cm. Thereafter,a root mean square value is determined based on the Equation 1 below.

d _(rms)=Sqrt{((d1)²+(d2)²+(d3)² . . . (dn)²)/N]  Equation 1

in an exemplary embodiment, if the d_(rms) value is less than 3 cm foreach of the cluster in each ROI then the Lidar sensor data 203 isdetected as correct. Otherwise, the Lidar sensor data 203 is detected asincorrect.

At block 309, the method may include determining correctness of Lidarsensor data 203 based on the distance value. If the distance value isless than a predefined threshold value, the processor 107 may detect theLidar sensor data 203 as correct. If the distance value is greater thanthe predefined threshold value, the processor 107 may detect the Lidarsensor data 203 as incorrect. Once the Lidar sensor data 203 is detectedas incorrect, the processor 107 may provide information regardingincorrect Lidar sensor data 203 to a navigation module of the autonomousvehicle 101 to discontinue the navigation or to look for a safe parkingon roadside of the vehicle 101.

Computer System

FIG. 4 illustrates a block diagram of an exemplary computer system 400for implementing embodiments consistent with the present disclosure. Inan embodiment, the computer system 400 may be a detection system 105 fordetermining correctness of Lidar sensor data 203 used for localizingautonomous vehicle 101. The computer system 400 may include a centralprocessing unit (“CPU” or “processor”) 402. The processor 402 maycomprise at least one data processor for executing program componentsfor executing user or system-generated business processes. The processor402 may include specialized processing units such as integrated system(bus) controllers, memory management control units, floating pointunits, graphics processing units, digital signal processing units, etc.

The processor 402 may be disposed in communication with one or moreinput/output (I/O) devices (411 and 412) via I/O interface 401. The I/Ointerface 401 may employ communication protocols/methods such as,without limitation, audio, analog, digital, stereo, IEEE-1394, serialbus, Universal Serial Bus (USB), infrared, PS/2, BNC, coaxial,component, composite, Digital Visual Interface (DVI), high-definitionmultimedia interface (HDMI), Radio Frequency (RF) antennas, S-Video,Video Graphics Array (VGA), IEEE 802.n/b/g/n/x, Bluetooth, cellular(e.g., Code-Division Multiple Access (CDMA), High-Speed Packet Access(HSPA+), Global System For Mobile Communications (GSM), Long-TermEvolution (LTE) or the like), etc. Using the I/O interface 401, thecomputer system 400 may communicate with one or more I/O devices 411 and412.

In some embodiments, the processor 402 may be disposed in communicationwith a communication network 409 via a network interface 403. Thenetwork interface 403 may communicate with the communication network409. The network interface 403 may employ connection protocolsincluding, without limitation, direct connect, Ethernet (e.g., twistedpair 10/100/1000 Base T), Transmission Control Protocol/InternetProtocol (TCP/IP), token ring, IEEE 802.11a/b/g/n/x, etc.

The communication network 409 can be implemented as one of the severaltypes of networks, such as intranet or Local Area Network (LAN) and suchwithin the organization. The communication network 409 may either be adedicated network or a shared network, which represents an associationof several types of networks that use a variety of protocols, forexample, Hypertext Transfer Protocol (HTTP), Transmission ControlProtocol/Internet Protocol (TCP/IP), Wireless Application Protocol(WAP), etc., to communicate with each other. Further, the communicationnetwork 409 may include a variety of network devices, including routers,bridges, servers, computing devices, storage devices, etc.

In some embodiments, the processor 402 may be disposed in communicationwith a memory 405 (e.g., RAM 413, ROM 414, etc. as shown in FIG. 4) viaa storage interface 404. The storage interface 404 may connect to memory405 including, without limitation, memory drives, removable disc drives,etc., employing connection protocols such as Serial Advanced TechnologyAttachment (SATA), Integrated Drive Electronics (IDE), IEEE-1394,Universal Serial Bus (USB), fiber channel, Small Computer SystemsInterface (SCSI), etc. The memory drives may further include a drum,magnetic disc drive, magneto-optical drive, optical drive, RedundantArray of Independent Discs (RAID), solid-state memory devices,solid-state drives, etc.

The memory 405 may store a collection of program or database components,including, without limitation, user/application 406, an operating system407, a web browser 408, mail client 415, mail server 416, web server 417and the like. In some embodiments, computer system 400 may storeuser/application data 406, such as the data, variables, records, etc. asdescribed in this invention. Such databases may be implemented asfault-tolerant, relational, scalable, secure databases such as Oracle®or Sybase®.

The operating system 407 may facilitate resource management andoperation of the computer system 400. Examples of operating systems 407include, without limitation, APPLE MACINTOSH® OS X, UNIX®, UNIX-likesystem distributions (E.G., BERKELEY SOFTWARE DISTRIBUTION™ (BSD),FREEBSD™, NETBSD™, OPENBSD™, etc.), LINUX DISTRIBUTIONS™ (E.G., REDHAT™, UBUNTU™, KUBUNTU™, etc.), IBM™ OS/2, MICROSOFT™ WINDOWS™ (XP™,VISTA™/7/8, 10 etc.), APPLE® IOS™, GOOGLE® ANDROID™, BLACKBERRY® OS, orthe like. A user interface may facilitate display, execution,interaction, manipulation, or operation of program components throughtextual or graphical facilities. For example, user interfaces mayprovide computer interaction interface elements on a display systemoperatively connected to the computer system 400, such as cursors,icons, check boxes, menus, windows, widgets, etc. Graphical UserInterfaces (GUIs) may be employed, including, without limitation, APPLEMACINTOSH® operating systems, IBM™ OS/2, MICROSOFT™ WINDOWS™ (XP™,VISTA™/7/8, 10 etc.), Unix′ X-Windows, web interface libraries (e.g.,AJAX™, DHTML™, ADOBE FLASH™, JAVASCRIPT™, JAVA™, etc.), or the like.

Furthermore, one or more computer-readable storage media may be utilizedin implementing embodiments consistent with the present invention. Acomputer-readable storage medium refers to any type of physical memoryon which information or data readable by a processor may be stored.Thus, a computer-readable storage medium may store instructions forexecution by one or more processors, including instructions for causingthe processor(s) to perform steps or stages consistent with theembodiments described herein. The term “computer-readable medium” shouldbe understood to include tangible items and exclude carrier waves andtransient signals, i.e., non-transitory. Examples include Random AccessMemory (RAM), Read-Only Memory (ROM), volatile memory, nonvolatilememory, hard drives, Compact Disc (CD) ROMs, Digital Video Disc (DVDs),flash drives, disks, and any other known physical storage media.

Advantages of the embodiment of the present disclosure are illustratedherein.

In an embodiment, the present disclosure provides a method and systemfor determining correctness of lidar sensor data used for localizingautonomous vehicle

In an embodiment, since present disclosure implements pattern formingwhich is compared with 2D data, it involves less computational overheadfor determining correctness of Lidar sensor data.

In an embodiment, the present disclosure determines correctness of Lidarsensor data quickly by understanding the environment of the vehicle andhence aids the vehicle to decide whether to continue navigation or stopmoving.

The terms “an embodiment”, “embodiment”, “embodiments”, “theembodiment”, “the embodiments”, “one or more embodiments”, “someembodiments”, and “one embodiment” mean “one or more (but not all)embodiments of the invention(s)” unless expressly specified otherwise.

The terms “including”, “comprising”, “having” and variations thereofmean “including but not limited to”, unless expressly specifiedotherwise. The enumerated listing of items does not imply that any orall the items are mutually exclusive, unless expressly specifiedotherwise.

The terms “a”, “an” and “the” mean “one or more”, unless expresslyspecified otherwise.

A description of an embodiment with several components in communicationwith each other does not imply that all such components are required. Onthe contrary, a variety of optional components are described toillustrate the wide variety of possible embodiments of the invention.

When a single device or article is described herein, it will be clearthat more than one device/article (whether they cooperate) may be usedin place of a single device/article. Similarly, where more than onedevice or article is described herein (whether they cooperate), it willbe clear that a single device/article may be used in place of the morethan one device or article or a different number of devices/articles maybe used instead of the shown number of devices or programs. Thefunctionality and/or the features of a device may be alternativelyembodied by one or more other devices which are not explicitly describedas having such functionality/features. Thus, other embodiments of theinvention need not include the device itself.

Finally, the language used in the specification has been principallyselected for readability and instructional purposes, and it may not havebeen selected to delineate or circumscribe the inventive subject matter.It is therefore intended that the scope of the invention be limited notby this detailed description, but rather by any claims that issue on anapplication based here on. Accordingly, the embodiments of the presentinvention are intended to be illustrative, but not limiting, of thescope of the invention, which is set forth in the following claims.

While various aspects and embodiments have been disclosed herein, otheraspects and embodiments will be apparent to those skilled in the art.The various aspects and embodiments disclosed herein are for purposes ofillustration and are not intended to be limiting, with the true scopeand spirit being indicated by the following claims.

Referral Numerals: Reference Number Description 100 Architecture 101Autonomous vehicle 103 Database 105 Detection system 107 Processor 109I/O interface 111 Memory 200 Data 201 ROI data 203 Lidar sensor data 205Cluster data 209 Other data 210 Modules 211 ROI identification module213 Lidar sensor data obtaining module 215 Cluster forming module 217Distance value identification module 219 Correctness determinationmodule 221 Other modules 223 Reference navigation path 250 LIDAR Sensor252 Reflection points 400 Exemplary computer system 401 I/O Interface ofthe exemplary computer system 402 Processor of the exemplary computersystem 403 Network interface 404 Storage interface 405 Memory of theexemplary computer system 406 User/Application 407 Operating system 408Web browser 409 Communication network 411 Input devices 412 Outputdevices 413 RAM 414 ROM 415 Mail Client 416 Mail Server 417 Web Server

What is claimed is:
 1. A method of determining correctness of Lidarsensor data used for localizing an autonomous vehicle, the methodcomprising: identifying, by a detection system, one or more Region ofInterests (ROIs) in Field of View (FOV) of Lidar sensors of anautonomous vehicle along a navigation path; obtaining, by the detectionsystem, Lidar sensor data comprising one or more reflection points, ineach of the one or more ROIs, wherein the one or more reflection pointscorrespond to one or more objects in each of the one or more ROIs;forming, by the detection system, one or more clusters of one or morereflection points in each of the one or more ROIs; identifying, by thedetection system, a distance value between the one or more clustersprojected on a 2D map of an environment and corresponding navigation mapobstacle points, for each of the one or more ROIs; and determining, bythe detection system, correctness of the Lidar sensor data based on thedistance value.
 2. The method as claimed in claim 1, wherein the one ormore clusters is of a pattern comprising at least one of straight line,arc, L-shape, or multiple bend shape.
 3. The method as claimed in claim1, wherein the Lidar sensor data is detected as correct when thedistance value is less than a predefined threshold value for apredetermined percentage value of each of the identified clusters. 4.The method as claimed in claim 1 further comprising providinginformation to a navigation module of the autonomous vehicle todiscontinue navigation when the Lidar sensor data is incorrect.
 5. Themethod as claimed in claim 2, wherein each of the one or more clustersare formed by joining the one or more reflection points until thepattern is formed which is of a predefined length.
 6. The method asclaimed in claim 1, wherein identifying the one or more ROIs comprises:obtaining a reference navigation path from a current autonomous vehicleposition to a destination position, wherein the reference navigationpath comprises one or more path points; obtaining the one or more pathpoints in a predefined distance range in the reference navigation path;identifying one or more tangents by forming a straight line ofpredefined number of path points; and identifying the one or more ROIsin a direction of a perpendicular line from each of the one or moretangents.
 7. A detection system for determining correctness of Lidarsensor data used for localizing an autonomous vehicle, the detectionsystem comprising: a processor; and a memory communicatively coupled tothe processor, wherein the memory stores processor-executableinstructions, which, on execution, causes the processor to: identify oneor more Region of Interests (ROIs) in Field of View (FOV) of Lidarsensors of an autonomous vehicle along a navigation path; obtain Lidarsensor data comprising one or more reflection points, in each of the oneor more ROIs, wherein the one or more reflection points correspond toone or more objects in each of the one or more ROIs; form one or moreclusters of one or more reflection points in each of the one or moreROIs; identify a distance value between the one or more clustersprojected on a 2D map of an environment and corresponding navigation mapobstacle points, for each of the one or more ROIs; and determinecorrectness of the Lidar sensor data based on the distance value.
 8. Thedetection system as claimed in claim 7, wherein the one or more clustersis of a pattern comprising at least one of straight line, arc, L-shape,or multiple bend shape.
 9. The detection system as claimed in claim 7,wherein the processor detects the Lidar sensor data as correct when thedistance value is less than a predefined threshold value for apredetermined percentage value of each of the identified clusters. 10.The detection system as claimed in claim 7, wherein the processorfurther provides information to a navigation module of the autonomousvehicle to discontinue navigation when the Lidar sensor data isincorrect.
 11. The detection system as claimed in claim 8, wherein theprocessor forms each of the one or more clusters by joining the one ormore reflection points until the pattern is formed which is of apredefined length.
 12. The detection system as claimed in claim 7,wherein the processor identifies the one or more ROIs by performingsteps of: obtaining a reference navigation path from a currentautonomous vehicle position to a destination position, wherein thereference navigation path comprises one or more path points; obtainingthe one or more path points in a predefined distance range in thereference navigation path; identifying one or more tangents by forming astraight line of predefined number of path points; and identifying theone or more ROIs in a direction of a perpendicular line from each of theone or more tangents.
 13. A non-transitory computer readable mediumincluding instructions stored thereon that when processed by at leastone processor causes detection system to determine correctness of Lidarsensor data used for localizing an autonomous vehicle by performingoperations comprising: identifying one or more Region of Interests(ROIs) in Field of View (FOV) of Lidar sensors of an autonomous vehiclealong a navigation path; obtaining Lidar sensor data comprising one ormore reflection points, in each of the one or more ROIs, wherein the oneor more reflection points correspond to one or more objects in each ofthe one or more ROIs; forming one or more clusters of one or morereflection points in each of the one or more ROIs; identifying adistance value between the one or more clusters projected on a 2D map ofan environment and corresponding navigation map obstacle points, foreach of the one or more ROIs; and determining correctness of the Lidarsensor data based on the distance value.